Antenor Services 01 of 07 Predictive Models
Machine learning consulting services
Machine learning that pays for itself. We build forecasting, predictive maintenance and market prediction models against a success metric agreed before we write code, and we keep watching them after launch.
Who this is for
- Operations, supply chain and finance leaders sitting on years of ERP, sensor or transaction data that nobody predicts with.
- Mid-market companies where demand, failures and prices are still forecast by gut and spreadsheet.
- Teams burned by an AI pilot that impressed in the demo and never reached production.
The problem we remove
Most ML projects die between the notebook and the P&L. Our fix is structural: a data audit before any promise, a pilot measured against a number you chose, and production engineering by people who have shipped systems for Telefónica, MercadoLibre, IBM and Siemens. Measurable value, or we tell you early.
What we build
Demand forecasting and predictive analytics
Forecasts wired into the decisions they exist for: purchasing, staffing, inventory, cash. We build demand forecasting models on your historical data and connect them to your planning workflow, so the prediction changes what you order on Monday, not what a dashboard shows on Friday.
Predictive maintenance with machine learning
Models that read sensor and maintenance history to flag failures before they stop the line. Predictive maintenance is one of the highest-ROI applications of machine learning in operations, and one of the most data-sensitive, which is why every engagement starts with the data audit.
Weather nowcasting and environmental prediction
Short-horizon prediction from radar and satellite data, minutes to hours ahead. We built a convective nowcasting system on SINARAME radar and GOES-19 imagery, published as a research preprint. If your operation depends on weather windows, this is a capability almost nobody offers commercially.
Market prediction models
Trend-identification models for financial markets, built with the same discipline as our industrial work: rigorous validation, no overfit heroics. Our Nasdaq trend method is offered to the public through Asesores Inteligentes, a supervised roboadvisor. More on how we evaluate ML strategies honestly in Beyond Alpha.
Proof, not promises
Two of our own research-grade systems, both verifiable.
Convective weather nowcasting
Deep learning models predicting severe convective weather minutes to hours ahead from SINARAME radar and GOES-19 satellite data, reaching a Critical Success Index of 0.39 at +60 minutes against the S-PROG baseline.
Published method / US fundNasdaq trend models
Trading models that identify trends in Nasdaq equities, validated out-of-sample and offered to the public through Asesores Inteligentes, a supervised roboadvisor with SIPC-insured US custody.
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Discovery
We map the process, the constraint and the money attached to it.
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Data Audit
We test whether your data can carry the model before promising results.
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Proof of Concept
A pilot built against a success metric agreed before we write code.
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Production
Deployed into your stack with your team, not handed off as slides.
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Monitoring
Models drift. We keep watching them after launch, not just at delivery.
Straight answers
The questions buyers actually ask, answered with numbers where they exist.
How much does it cost to build a machine learning model?
For mid-market companies, a production-grade model typically runs $15k to $80k depending on data readiness. Data preparation is usually 40 to 60 percent of the effort, which is why we audit your data before quoting. After the audit you get a fixed scope, not an open-ended retainer.
How long does a machine learning project take?
A proof of concept takes 3 to 6 weeks and a production deployment 8 to 16 weeks. Data quality is the main variable: clean, well-labeled data keeps you at the low end, messy data extends the audit and preparation phases.
How much data do we need to start?
Less than most vendors imply. For tabular business problems, thousands of historical records are often enough; what matters is that the data reflects the decision you want to improve. The data audit answers this precisely before you spend anything on modeling.
What ROI does predictive maintenance deliver?
Industry studies report up to 70 percent less unplanned downtime and 10 to 20 percent savings in spare parts and maintenance labor. Your number depends on failure frequency and the cost of an hour of downtime, which is exactly what the proof of concept quantifies against a metric agreed upfront.
Do we need an in-house data science team to maintain the models?
No. We deploy every model with drift monitoring and retraining pipelines, and we either operate it for you or hand it to your engineers with full documentation. Most clients start on our monitoring and take over once the system has earned their trust.
Bring us the bottleneck.
One session with a senior engineer. We'll tell you whether AI pays for it, and what it takes to ship.